In the literature, some attempts have been made to develop supervised and semi-supervised approaches [1]}, [2]}. Nevertheless, the task of time-series anomaly detection is usually formulated as an unsupervised problem [3]}. In fact, since anomalies occur rarely, annotating data becomes challenging and costly. In this article, we focus on the topic of unsupervised anomaly detection in time-series.
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